Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

Domingo, Darío; Alonso, Rafael; Lamelas, María Teresa; Montealegre, Antonio Luis; Rodríguez, Francisco; de la Riva, Juan

doi:10.3390/rs11030261

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Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

Darío Domingo

^1,*

Rafael Alonso

^2,3,

María Teresa Lamelas

^1,4

Antonio Luis Montealegre

Francisco Rodríguez

^2,3,5

and

Juan de la Riva

GEOFOREST-IUCA, Department of Geography, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain

föra forest technologies sll, Campus Duques de Soria s/n, 42004 Soria, Spain

Sustainable Forest Management Research Institute University of Valladolid-INIA, Campus Duques de Soria s/n, 42004 Soria, Spain

⁴

Centro Universitario de la Defensa de Zaragoza, Academia General Militar, Ctra. de Huesca s/n, 50090 Zaragoza, Spain

⁵

EU Ingenierías Agrarias, Campus Duques de Soria s/n, Universidad de Valladolid, 42004 Soria, Spain

Author to whom correspondence should be addressed.

Remote Sens. 2019, 11(3), 261; https://doi.org/10.3390/rs11030261

Received: 6 November 2018 / Revised: 13 December 2018 / Accepted: 21 January 2019 / Published: 28 January 2019

(This article belongs to the Special Issue Lidar for Ecosystem Science and Management)

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Abstract

This study assesses model temporal transferability using airborne laser scanning (ALS) data acquired over two different dates. Seven forest attributes (i.e. stand density, basal area, squared mean diameter, dominant diameter, tree dominant height, timber volume, and total tree biomass) were estimated using an area-based approach in Mediterranean Aleppo pine forests. Low-density ALS data were acquired in 2011 and 2016 while 147 forest inventory plots were measured in 2013, 2014, and 2016. Single-tree growth models were used to generate concomitant field data for 2011 and 2016. A comparison of five selection techniques and five regression methods were performed to regress field observations against ALS metrics. The selection of the best regression models fitted for each stand attribute, and separately for both 2011 and 2016, was performed following an indirect approach. Model performance and temporal transferability were analyzed by extrapolating the best fitted models from 2011 to 2016 and inversely from 2016 to 2011 using the direct approach. Non-parametric support vector machine with radial kernel was the best regression method with average relative % root mean square error differences of 2.13% for 2011 models and 1.58% for 2016 ones. View Full-Text

Keywords: model temporal transferability; ALS; forest inventory; backdating; Mediterranean forest

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MDPI and ACS Style

Domingo, D.; Alonso, R.; Lamelas, M.T.; Montealegre, A.L.; Rodríguez, F.; de la Riva, J. Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data. Remote Sens. 2019, 11, 261. https://doi.org/10.3390/rs11030261

AMA Style

Domingo D, Alonso R, Lamelas MT, Montealegre AL, Rodríguez F, de la Riva J. Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data. Remote Sensing. 2019; 11(3):261. https://doi.org/10.3390/rs11030261

Chicago/Turabian Style

Domingo, Darío, Rafael Alonso, María Teresa Lamelas, Antonio Luis Montealegre, Francisco Rodríguez, and Juan de la Riva. 2019. "Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data" Remote Sensing 11, no. 3: 261. https://doi.org/10.3390/rs11030261

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Temporal Transferability of Pine Forest Attributes Modeling Using Low-Density Airborne Laser Scanning Data

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